{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[30m\u001b[43mbike_share\u001b[m\u001b[m/     \u001b[30m\u001b[43mctr\u001b[m\u001b[m/            \u001b[34msina_weibo\u001b[m\u001b[m/     \u001b[30m\u001b[43myhq\u001b[m\u001b[m/\r\n",
      "\u001b[30m\u001b[43mcreditcard\u001b[m\u001b[m/     \u001b[30m\u001b[43mhouse_price\u001b[m\u001b[m/    viewlook.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 01 o2o  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "o2o_data = pd.read_csv('yhq/train01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 492696 entries, 0 to 492695\n",
      "Data columns (total 56 columns):\n",
      "user_id                                         492696 non-null int64\n",
      "discount_rate                                   492696 non-null float64\n",
      "distance                                        437588 non-null float64\n",
      "day_of_month                                    492696 non-null int64\n",
      "days_distance                                   492696 non-null int64\n",
      "discount_man                                    484630 non-null float64\n",
      "discount_jian                                   484630 non-null float64\n",
      "is_man_jian                                     492696 non-null int64\n",
      "total_sales                                     486585 non-null float64\n",
      "sales_use_coupon                                489975 non-null float64\n",
      "total_coupon                                    489975 non-null float64\n",
      "merchant_min_distance                           393842 non-null float64\n",
      "merchant_max_distance                           393842 non-null float64\n",
      "merchant_mean_distance                          393842 non-null float64\n",
      "merchant_median_distance                        393842 non-null float64\n",
      "merchant_coupon_transfer_rate                   450109 non-null float64\n",
      "coupon_rate                                     486585 non-null float64\n",
      "count_merchant                                  275177 non-null float64\n",
      "user_min_distance                               39322 non-null float64\n",
      "user_max_distance                               39322 non-null float64\n",
      "user_mean_distance                              39322 non-null float64\n",
      "user_median_distance                            39322 non-null float64\n",
      "buy_use_coupon                                  275177 non-null float64\n",
      "buy_total                                       275177 non-null float64\n",
      "coupon_received                                 275177 non-null float64\n",
      "avg_user_date_datereceived_gap                  42413 non-null float64\n",
      "min_user_date_datereceived_gap                  42413 non-null float64\n",
      "max_user_date_datereceived_gap                  42413 non-null float64\n",
      "buy_use_coupon_rate                             209206 non-null float64\n",
      "user_coupon_transfer_rate                       212649 non-null float64\n",
      "user_merchant_buy_total                         492696 non-null float64\n",
      "user_merchant_received                          492696 non-null float64\n",
      "user_merchant_buy_use_coupon                    135134 non-null float64\n",
      "user_merchant_any                               492696 non-null float64\n",
      "user_merchant_buy_common                        135134 non-null float64\n",
      "user_merchant_coupon_transfer_rate              68101 non-null float64\n",
      "user_merchant_coupon_buy_rate                   117225 non-null float64\n",
      "user_merchant_rate                              117225 non-null float64\n",
      "user_merchant_common_buy_rate                   117225 non-null float64\n",
      "this_month_user_receive_same_coupon_count       492696 non-null int64\n",
      "this_month_user_receive_all_coupon_count        492696 non-null int64\n",
      "this_month_user_receive_same_coupon_lastone     492696 non-null int64\n",
      "this_month_user_receive_same_coupon_firstone    492696 non-null int64\n",
      "this_day_user_receive_all_coupon_count          492696 non-null int64\n",
      "this_day_user_receive_same_coupon_count         492696 non-null int64\n",
      "day_gap_before                                  492696 non-null int64\n",
      "day_gap_after                                   492696 non-null int64\n",
      "is_weekend                                      492696 non-null int64\n",
      "weekday1                                        492696 non-null int64\n",
      "weekday2                                        492696 non-null int64\n",
      "weekday3                                        492696 non-null int64\n",
      "weekday4                                        492696 non-null int64\n",
      "weekday5                                        492696 non-null int64\n",
      "weekday6                                        492696 non-null int64\n",
      "weekday7                                        492696 non-null int64\n",
      "label                                           492696 non-null int64\n",
      "dtypes: float64(35), int64(21)\n",
      "memory usage: 210.5 MB\n"
     ]
    }
   ],
   "source": [
    "o2o_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "o2o_test_data = pd.read_csv('yhq/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 222261 entries, 0 to 222260\n",
      "Data columns (total 55 columns):\n",
      "user_id                                         222261 non-null int64\n",
      "coupon_id                                       222261 non-null int64\n",
      "discount_rate                                   222261 non-null float64\n",
      "distance                                        198763 non-null float64\n",
      "date_received                                   222261 non-null int64\n",
      "day_of_month                                    222261 non-null int64\n",
      "days_distance                                   222261 non-null int64\n",
      "discount_man                                    217257 non-null float64\n",
      "discount_jian                                   217257 non-null float64\n",
      "is_man_jian                                     222261 non-null int64\n",
      "total_sales                                     221584 non-null float64\n",
      "sales_use_coupon                                222256 non-null float64\n",
      "total_coupon                                    222256 non-null float64\n",
      "merchant_min_distance                           200124 non-null float64\n",
      "merchant_max_distance                           200124 non-null float64\n",
      "merchant_mean_distance                          200124 non-null float64\n",
      "merchant_median_distance                        200124 non-null float64\n",
      "merchant_coupon_transfer_rate                   219848 non-null float64\n",
      "coupon_rate                                     221584 non-null float64\n",
      "count_merchant                                  197888 non-null float64\n",
      "user_min_distance                               35092 non-null float64\n",
      "user_max_distance                               35092 non-null float64\n",
      "user_mean_distance                              35092 non-null float64\n",
      "user_median_distance                            35092 non-null float64\n",
      "buy_use_coupon                                  197888 non-null float64\n",
      "buy_total                                       197888 non-null float64\n",
      "coupon_received                                 197888 non-null float64\n",
      "avg_user_date_datereceived_gap                  38067 non-null float64\n",
      "min_user_date_datereceived_gap                  38067 non-null float64\n",
      "max_user_date_datereceived_gap                  38067 non-null float64\n",
      "buy_use_coupon_rate                             127845 non-null float64\n",
      "user_coupon_transfer_rate                       181054 non-null float64\n",
      "user_merchant_buy_total                         222261 non-null float64\n",
      "user_merchant_received                          222261 non-null float64\n",
      "user_merchant_buy_use_coupon                    73402 non-null float64\n",
      "user_merchant_any                               222261 non-null float64\n",
      "user_merchant_buy_common                        73402 non-null float64\n",
      "user_merchant_coupon_transfer_rate              56912 non-null float64\n",
      "user_merchant_coupon_buy_rate                   44869 non-null float64\n",
      "user_merchant_rate                              44869 non-null float64\n",
      "user_merchant_common_buy_rate                   44869 non-null float64\n",
      "this_month_user_receive_same_coupon_count       222261 non-null int64\n",
      "this_month_user_receive_all_coupon_count        222261 non-null int64\n",
      "this_month_user_receive_same_coupon_lastone     222261 non-null int64\n",
      "this_month_user_receive_same_coupon_firstone    222261 non-null int64\n",
      "this_day_user_receive_all_coupon_count          222261 non-null int64\n",
      "this_day_user_receive_same_coupon_count         222261 non-null int64\n",
      "is_weekend                                      222261 non-null int64\n",
      "weekday1                                        222261 non-null int64\n",
      "weekday2                                        222261 non-null int64\n",
      "weekday3                                        222261 non-null int64\n",
      "weekday4                                        222261 non-null int64\n",
      "weekday5                                        222261 non-null int64\n",
      "weekday6                                        222261 non-null int64\n",
      "weekday7                                        222261 non-null int64\n",
      "dtypes: float64(35), int64(20)\n",
      "memory usage: 93.3 MB\n"
     ]
    }
   ],
   "source": [
    "o2o_test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cf077f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2s1i0GTgsEY+tsh3EBk2V9CB/8IGPSrtijc1kO3r+qPKwyTQ+dVDy6rFVXtoQHKbtu54/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\nhIyKW0QkZFTcIiIho+IWEQmZ/wfNpXUNaLBcVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cef90f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o2o_data['label'].value_counts().plot.pie(labeldistance = 1.1, autopct ='%1.2f%%',\n",
    "                                         shadow = False, startangle = 90, pctdistance = 0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 02 共享单车"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikeshare_data = pd.read_csv('bike_share/train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 16 columns):\n",
      "Unnamed: 0    10886 non-null int64\n",
      "datetime      10886 non-null object\n",
      "season        10886 non-null int64\n",
      "holiday       10886 non-null int64\n",
      "workingday    10886 non-null int64\n",
      "weather       10886 non-null int64\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null int64\n",
      "month         10886 non-null int64\n",
      "day           10886 non-null int64\n",
      "hour          10886 non-null int64\n",
      "dtypes: float64(3), int64(12), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "bikeshare_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cedc828>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U91QtxtJDyWwdscCR/nca1F/NK/P3c8GBhTRms3J/4zZ+OfEruFHYhPTDVeyz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P436nrdLp65/X1zwltDnR134QS4+Xf3s3HH+e8G7k2zhG+h7mJTRV1I6uuo6982ok6/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YLISSHVkkuTkwDgFQVrQhhhAGCCJAKaEefLdAtEEQAYo1AypkUQCQAmO1ku5StqDhjTRlIw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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d7f3978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 需要预测的 输出 \n",
    "#bikeshare_data['count']\n",
    "#bikeshare_data['count'].value_counts().plot.pie(labeldistance = 1.1, autopct ='%1.2f%%',\n",
    "                                         shadow = False, startangle = 90, pctdistance = 0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      169\n",
       "4      149\n",
       "3      144\n",
       "6      135\n",
       "2      132\n",
       "7      118\n",
       "1      105\n",
       "8       99\n",
       "10      95\n",
       "11      95\n",
       "9       83\n",
       "12      76\n",
       "16      73\n",
       "14      66\n",
       "13      65\n",
       "20      61\n",
       "17      58\n",
       "15      57\n",
       "21      54\n",
       "28      54\n",
       "23      53\n",
       "26      48\n",
       "24      44\n",
       "64      43\n",
       "31      43\n",
       "18      42\n",
       "25      42\n",
       "33      41\n",
       "32      39\n",
       "30      39\n",
       "      ... \n",
       "625      1\n",
       "818      1\n",
       "842      1\n",
       "850      1\n",
       "827      1\n",
       "700      1\n",
       "660      1\n",
       "652      1\n",
       "636      1\n",
       "612      1\n",
       "588      1\n",
       "532      1\n",
       "891      1\n",
       "867      1\n",
       "851      1\n",
       "887      1\n",
       "819      1\n",
       "890      1\n",
       "779      1\n",
       "771      1\n",
       "755      1\n",
       "747      1\n",
       "739      1\n",
       "691      1\n",
       "683      1\n",
       "667      1\n",
       "603      1\n",
       "587      1\n",
       "970      1\n",
       "843      1\n",
       "Name: count, Length: 822, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikeshare_data['count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bk_test = pd.read_csv(\"bike_share/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6493 entries, 0 to 6492\n",
      "Data columns (total 9 columns):\n",
      "datetime      6493 non-null object\n",
      "season        6493 non-null int64\n",
      "holiday       6493 non-null int64\n",
      "workingday    6493 non-null int64\n",
      "weather       6493 non-null int64\n",
      "temp          6493 non-null float64\n",
      "atemp         6493 non-null float64\n",
      "humidity      6493 non-null int64\n",
      "windspeed     6493 non-null float64\n",
      "dtypes: float64(3), int64(5), object(1)\n",
      "memory usage: 456.6+ KB\n"
     ]
    }
   ],
   "source": [
    "bk_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 03  CTR  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ctr_data = pd.read_csv('ctr/train_small.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99999 entries, 0 to 99998\n",
      "Data columns (total 24 columns):\n",
      "id                  99999 non-null float64\n",
      "click               99999 non-null int64\n",
      "hour                99999 non-null int64\n",
      "C1                  99999 non-null int64\n",
      "banner_pos          99999 non-null int64\n",
      "site_id             99999 non-null object\n",
      "site_domain         99999 non-null object\n",
      "site_category       99999 non-null object\n",
      "app_id              99999 non-null object\n",
      "app_domain          99999 non-null object\n",
      "app_category        99999 non-null object\n",
      "device_id           99999 non-null object\n",
      "device_ip           99999 non-null object\n",
      "device_model        99999 non-null object\n",
      "device_type         99999 non-null int64\n",
      "device_conn_type    99999 non-null int64\n",
      "C14                 99999 non-null int64\n",
      "C15                 99999 non-null int64\n",
      "C16                 99999 non-null int64\n",
      "C17                 99999 non-null int64\n",
      "C18                 99999 non-null int64\n",
      "C19                 99999 non-null int64\n",
      "C20                 99999 non-null int64\n",
      "C21                 99999 non-null int64\n",
      "dtypes: float64(1), int64(14), object(9)\n",
      "memory usage: 18.3+ MB\n"
     ]
    }
   ],
   "source": [
    "ctr_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>click</th>\n",
       "      <th>hour</th>\n",
       "      <th>C1</th>\n",
       "      <th>banner_pos</th>\n",
       "      <th>site_id</th>\n",
       "      <th>site_domain</th>\n",
       "      <th>site_category</th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_domain</th>\n",
       "      <th>...</th>\n",
       "      <th>device_type</th>\n",
       "      <th>device_conn_type</th>\n",
       "      <th>C14</th>\n",
       "      <th>C15</th>\n",
       "      <th>C16</th>\n",
       "      <th>C17</th>\n",
       "      <th>C18</th>\n",
       "      <th>C19</th>\n",
       "      <th>C20</th>\n",
       "      <th>C21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000009e+18</td>\n",
       "      <td>0</td>\n",
       "      <td>14102100</td>\n",
       "      <td>1005</td>\n",
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       "      <td>15706</td>\n",
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       "      <td>50</td>\n",
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       "      <td>-1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000017e+19</td>\n",
       "      <td>0</td>\n",
       "      <td>14102100</td>\n",
       "      <td>1005</td>\n",
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       "      <td>1fbe01fe</td>\n",
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       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
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       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>100084</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000037e+19</td>\n",
       "      <td>0</td>\n",
       "      <td>14102100</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>1fbe01fe</td>\n",
       "      <td>f3845767</td>\n",
       "      <td>28905ebd</td>\n",
       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15704</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>100084</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  click      hour    C1  banner_pos   site_id site_domain  \\\n",
       "0  1.000009e+18      0  14102100  1005           0  1fbe01fe    f3845767   \n",
       "1  1.000017e+19      0  14102100  1005           0  1fbe01fe    f3845767   \n",
       "2  1.000037e+19      0  14102100  1005           0  1fbe01fe    f3845767   \n",
       "\n",
       "  site_category    app_id app_domain ...  device_type device_conn_type    C14  \\\n",
       "0      28905ebd  ecad2386   7801e8d9 ...            1                2  15706   \n",
       "1      28905ebd  ecad2386   7801e8d9 ...            1                0  15704   \n",
       "2      28905ebd  ecad2386   7801e8d9 ...            1                0  15704   \n",
       "\n",
       "   C15  C16   C17  C18  C19     C20  C21  \n",
       "0  320   50  1722    0   35      -1   79  \n",
       "1  320   50  1722    0   35  100084   79  \n",
       "2  320   50  1722    0   35  100084   79  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctr_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82509\n",
       "1    17490\n",
       "Name: click, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ctr_data['click']\n",
    "ctr_data['click'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d729b38>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0nntH5vD0XZkJe6ymel/bmUrEWNfzO6WS8bW2g7RGURd3eLdkvve0E2kTY1jzqek79/bM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\n4q8aNDlwvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d73dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctr_data['click'].value_counts().plot.pie(labeldistance = 1.1, autopct ='%1.2f%%',\n",
    "                                         shadow = False, startangle = 90, pctdistance = 0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ctr_test_data = pd.read_csv('ctr/test_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 23 columns):\n",
      "id                  10000 non-null uint64\n",
      "hour                10000 non-null int64\n",
      "C1                  10000 non-null int64\n",
      "banner_pos          10000 non-null int64\n",
      "site_id             10000 non-null object\n",
      "site_domain         10000 non-null object\n",
      "site_category       10000 non-null object\n",
      "app_id              10000 non-null object\n",
      "app_domain          10000 non-null object\n",
      "app_category        10000 non-null object\n",
      "device_id           10000 non-null object\n",
      "device_ip           10000 non-null object\n",
      "device_model        10000 non-null object\n",
      "device_type         10000 non-null int64\n",
      "device_conn_type    10000 non-null int64\n",
      "C14                 10000 non-null int64\n",
      "C15                 10000 non-null int64\n",
      "C16                 10000 non-null int64\n",
      "C17                 10000 non-null int64\n",
      "C18                 10000 non-null int64\n",
      "C19                 10000 non-null int64\n",
      "C20                 10000 non-null int64\n",
      "C21                 10000 non-null int64\n",
      "dtypes: int64(13), object(9), uint64(1)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "ctr_test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 04  墨尔本房价 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "houseprice_data = pd.read_csv('house_price/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "houseprice_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05 信用卡欺诈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "creditcard_data = pd.read_csv('creditcard/creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "creditcard_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 06  新浪微博互动预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sina_data = pd.read_csv('sina_weibo/new_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>分享 韧在 百度 文章</td>\n",
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       "   5237b0c81a2649de521adb1b7f08de1f  2015-04-17 11:21:29  1  0     打车  \\\n",
       "0  0369a3e9ce3c248c836545653f74b31d  2015-04-18 09:32:45  1  0  技术 晨读   \n",
       "1  5e4c19485cb70b5f2008b85f7689f803  2015-07-03 10:18:25  0  1   None   \n",
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       "\n",
       "  打车 一号 专车 代金券 来袭 妈妈 关心 一碗 热粥 情人 关心 一束 玫瑰 快快 关心 风雨 同行 善待 一条 路上 奉上 贴心 专车 券 礼包  \\\n",
       "0  技术 晨读 Java 集合 小抄 短 篇幅 里 List Map Set Queue 特征 ...                           \n",
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       "2           下载 音悦台 手机 客户端 最新 最全 清 MV 手中 看好 音乐吧 下载 地址                           \n",
       "3                               官方 媒体 报道 昂山 素季 访华 几个                           \n",
       "4                                        分享 韧在 百度 文章                           \n",
       "\n",
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     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sina_data.head()"
   ]
  }
 ],
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